Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells7485
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with TEMPHigh correlation
PM10 is highly overall correlated with CO and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 3 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 953 (2.7%) missing valuesMissing
PM10 has 777 (2.2%) missing valuesMissing
SO2 has 980 (2.8%) missing valuesMissing
NO2 has 1639 (4.7%) missing valuesMissing
CO has 1422 (4.1%) missing valuesMissing
O3 has 1151 (3.3%) missing valuesMissing
RAIN is highly skewed (γ1 = 26.5432332)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33518 (95.6%) zerosZeros

Reproduction

Analysis started2024-03-08 05:12:22.056855
Analysis finished2024-03-08 05:13:10.353492
Duration48.3 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:10.485112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:13:10.759958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:13:11.000049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:13:11.212303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:11.377133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:13:11.575670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:11.823577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:13:12.092929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:12.369432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:13:12.529943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct479
Distinct (%)1.4%
Missing953
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean69.626367
Minimum2
Maximum762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:12.790072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q117
median47
Q398
95-th percentile216
Maximum762
Range760
Interquartile range (IQR)81

Descriptive statistics

Standard deviation71.224916
Coefficient of variation (CV)1.022959
Kurtosis5.1869457
Mean69.626367
Median Absolute Deviation (MAD)34
Skewness1.922288
Sum2375025
Variance5072.9887
MonotonicityNot monotonic
2024-03-08T12:13:13.017416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1092
 
3.1%
8 625
 
1.8%
9 605
 
1.7%
7 599
 
1.7%
10 599
 
1.7%
12 594
 
1.7%
14 569
 
1.6%
13 564
 
1.6%
11 547
 
1.6%
6 535
 
1.5%
Other values (469) 27782
79.2%
(Missing) 953
 
2.7%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 1092
3.1%
4 376
 
1.1%
5 451
1.3%
6 535
1.5%
7 599
1.7%
8 625
1.8%
9 605
1.7%
10 599
1.7%
11 547
1.6%
ValueCountFrequency (%)
762 1
< 0.1%
683 1
< 0.1%
659 1
< 0.1%
559 1
< 0.1%
558 1
< 0.1%
556 1
< 0.1%
551 1
< 0.1%
545 1
< 0.1%
544 1
< 0.1%
538 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct556
Distinct (%)1.6%
Missing777
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean91.48269
Minimum2
Maximum993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:13.305402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q128
median69
Q3131
95-th percentile251
Maximum993
Range991
Interquartile range (IQR)103

Descriptive statistics

Standard deviation83.289578
Coefficient of variation (CV)0.91044085
Kurtosis6.9383673
Mean91.48269
Median Absolute Deviation (MAD)46
Skewness1.9083736
Sum3136667
Variance6937.1538
MonotonicityNot monotonic
2024-03-08T12:13:13.840054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 641
 
1.8%
5 528
 
1.5%
14 399
 
1.1%
12 380
 
1.1%
10 379
 
1.1%
17 370
 
1.1%
16 366
 
1.0%
9 365
 
1.0%
19 363
 
1.0%
15 359
 
1.0%
Other values (546) 30137
85.9%
(Missing) 777
 
2.2%
ValueCountFrequency (%)
2 3
 
< 0.1%
3 76
 
0.2%
4 21
 
0.1%
5 528
1.5%
6 641
1.8%
7 278
0.8%
8 348
1.0%
9 365
1.0%
9.8 1
 
< 0.1%
10 379
1.1%
ValueCountFrequency (%)
993 1
< 0.1%
991 1
< 0.1%
973 2
< 0.1%
948 1
< 0.1%
922 1
< 0.1%
917 1
< 0.1%
887 1
< 0.1%
835 1
< 0.1%
827 1
< 0.1%
750 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct247
Distinct (%)0.7%
Missing980
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean12.121553
Minimum0.2856
Maximum315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:14.095884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q12
median4
Q314
95-th percentile49
Maximum315
Range314.7144
Interquartile range (IQR)12

Descriptive statistics

Standard deviation18.896912
Coefficient of variation (CV)1.5589514
Kurtosis21.886752
Mean12.121553
Median Absolute Deviation (MAD)2
Skewness3.7248776
Sum413151.01
Variance357.09327
MonotonicityNot monotonic
2024-03-08T12:13:14.367969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 11723
33.4%
3 3558
 
10.1%
4 1814
 
5.2%
5 1449
 
4.1%
6 1286
 
3.7%
7 1022
 
2.9%
8 879
 
2.5%
9 765
 
2.2%
10 673
 
1.9%
11 618
 
1.8%
Other values (237) 10297
29.4%
(Missing) 980
 
2.8%
ValueCountFrequency (%)
0.2856 18
 
0.1%
0.5712 5
 
< 0.1%
0.8568 2
 
< 0.1%
1 469
 
1.3%
1.1424 2
 
< 0.1%
1.428 6
 
< 0.1%
1.7136 1
 
< 0.1%
1.9992 3
 
< 0.1%
2 11723
33.4%
2.2848 2
 
< 0.1%
ValueCountFrequency (%)
315 1
< 0.1%
314 1
< 0.1%
268 1
< 0.1%
254 1
< 0.1%
253 1
< 0.1%
224 1
< 0.1%
219 1
< 0.1%
216 1
< 0.1%
196 1
< 0.1%
193 2
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct296
Distinct (%)0.9%
Missing1639
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean32.49725
Minimum1.0265
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:14.705966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.0265
5-th percentile4
Q112
median25
Q346
95-th percentile85
Maximum231
Range229.9735
Interquartile range (IQR)34

Descriptive statistics

Standard deviation26.489531
Coefficient of variation (CV)0.81513146
Kurtosis2.3878609
Mean32.49725
Median Absolute Deviation (MAD)15
Skewness1.3853616
Sum1086220.6
Variance701.69525
MonotonicityNot monotonic
2024-03-08T12:13:14.935959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1036
 
3.0%
11 796
 
2.3%
6 796
 
2.3%
8 795
 
2.3%
12 777
 
2.2%
10 772
 
2.2%
14 769
 
2.2%
9 767
 
2.2%
7 761
 
2.2%
13 747
 
2.1%
Other values (286) 25409
72.5%
(Missing) 1639
 
4.7%
ValueCountFrequency (%)
1.0265 2
 
< 0.1%
1.4371 1
 
< 0.1%
1.6424 2
 
< 0.1%
2 1036
3.0%
2.053 1
 
< 0.1%
2.2583 1
 
< 0.1%
2.6689 1
 
< 0.1%
3 555
1.6%
3.4901 1
 
< 0.1%
4 678
1.9%
ValueCountFrequency (%)
231 1
< 0.1%
219 1
< 0.1%
205 1
< 0.1%
199 1
< 0.1%
198 1
< 0.1%
195 1
< 0.1%
189 1
< 0.1%
184 1
< 0.1%
183 1
< 0.1%
180 1
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct108
Distinct (%)0.3%
Missing1422
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean1022.5545
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:15.111622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1400
median800
Q31300
95-th percentile2600
Maximum10000
Range9900
Interquartile range (IQR)900

Descriptive statistics

Standard deviation898.73824
Coefficient of variation (CV)0.87891472
Kurtosis14.626095
Mean1022.5545
Median Absolute Deviation (MAD)400
Skewness2.8775555
Sum34400780
Variance807730.43
MonotonicityNot monotonic
2024-03-08T12:13:15.401189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 3255
 
9.3%
400 2921
 
8.3%
500 2697
 
7.7%
200 2386
 
6.8%
600 2269
 
6.5%
700 2223
 
6.3%
900 1857
 
5.3%
800 1842
 
5.3%
1000 1731
 
4.9%
1100 1467
 
4.2%
Other values (98) 10994
31.4%
(Missing) 1422
 
4.1%
ValueCountFrequency (%)
100 545
 
1.6%
200 2386
6.8%
300 3255
9.3%
400 2921
8.3%
500 2697
7.7%
600 2269
6.5%
700 2223
6.3%
800 1842
5.3%
900 1857
5.3%
1000 1731
4.9%
ValueCountFrequency (%)
10000 3
< 0.1%
9800 2
 
< 0.1%
9700 1
 
< 0.1%
9600 1
 
< 0.1%
9300 3
< 0.1%
9200 1
 
< 0.1%
9000 5
< 0.1%
8900 1
 
< 0.1%
8800 2
 
< 0.1%
8700 4
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct812
Distinct (%)2.4%
Missing1151
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean59.824713
Minimum0.2142
Maximum444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:15.645789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q118
median49
Q383
95-th percentile173
Maximum444
Range443.7858
Interquartile range (IQR)65

Descriptive statistics

Standard deviation54.605746
Coefficient of variation (CV)0.91276236
Kurtosis3.3599719
Mean59.824713
Median Absolute Deviation (MAD)32
Skewness1.5929856
Sum2028835.5
Variance2981.7875
MonotonicityNot monotonic
2024-03-08T12:13:15.906313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3300
 
9.4%
4 322
 
0.9%
16 315
 
0.9%
12 313
 
0.9%
3 307
 
0.9%
14 304
 
0.9%
64 304
 
0.9%
58 302
 
0.9%
18 301
 
0.9%
6 300
 
0.9%
Other values (802) 27845
79.4%
(Missing) 1151
 
3.3%
ValueCountFrequency (%)
0.2142 7
 
< 0.1%
0.4284 7
 
< 0.1%
0.6426 4
 
< 0.1%
0.8568 9
 
< 0.1%
1 167
0.5%
1.071 7
 
< 0.1%
1.2852 12
 
< 0.1%
1.4994 9
 
< 0.1%
1.7136 8
 
< 0.1%
1.9278 10
 
< 0.1%
ValueCountFrequency (%)
444 1
< 0.1%
413 1
< 0.1%
391 1
< 0.1%
386 1
< 0.1%
385 1
< 0.1%
379 2
< 0.1%
374 1
< 0.1%
371 1
< 0.1%
367 1
< 0.1%
364 1
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct1012
Distinct (%)2.9%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean12.445426
Minimum-19.9
Maximum40.3
Zeros191
Zeros (%)0.5%
Negative6684
Negative (%)19.1%
Memory size274.1 KiB
2024-03-08T12:13:16.186443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-19.9
5-th percentile-6.3
Q12.1
median13.6
Q322.3
95-th percentile30
Maximum40.3
Range60.2
Interquartile range (IQR)20.2

Descriptive statistics

Standard deviation11.751103
Coefficient of variation (CV)0.94421062
Kurtosis-1.0877016
Mean12.445426
Median Absolute Deviation (MAD)9.9
Skewness-0.14170269
Sum435751.69
Variance138.08842
MonotonicityNot monotonic
2024-03-08T12:13:16.408376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 204
 
0.6%
0 191
 
0.5%
-1 177
 
0.5%
-2 175
 
0.5%
2 165
 
0.5%
1 161
 
0.5%
-4 156
 
0.4%
21.8 140
 
0.4%
-5 139
 
0.4%
19.3 136
 
0.4%
Other values (1002) 33369
95.2%
ValueCountFrequency (%)
-19.9 1
< 0.1%
-19.7 1
< 0.1%
-19.5 1
< 0.1%
-18.9 1
< 0.1%
-18.7 1
< 0.1%
-18.5 1
< 0.1%
-18.1 1
< 0.1%
-17.9 1
< 0.1%
-17.4 1
< 0.1%
-17.3 1
< 0.1%
ValueCountFrequency (%)
40.3 1
< 0.1%
39 1
< 0.1%
38.9 1
< 0.1%
38.8 1
< 0.1%
38.7 2
< 0.1%
38.6 1
< 0.1%
38.5 2
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
37.9 1
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct589
Distinct (%)1.7%
Missing53
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1007.5986
Minimum982.8
Maximum1036.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:16.716119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum982.8
5-th percentile992.4
Q1999.3
median1007.3
Q31015.5
95-th percentile1024
Maximum1036.5
Range53.7
Interquartile range (IQR)16.2

Descriptive statistics

Standard deviation10.022101
Coefficient of variation (CV)0.0099465216
Kurtosis-0.86019444
Mean1007.5986
Median Absolute Deviation (MAD)8.1
Skewness0.11179248
Sum35277033
Variance100.44251
MonotonicityNot monotonic
2024-03-08T12:13:17.022009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1012 271
 
0.8%
1015 263
 
0.8%
1011 263
 
0.8%
1016 262
 
0.7%
1014 256
 
0.7%
1013 248
 
0.7%
1018 247
 
0.7%
1008 229
 
0.7%
1009 226
 
0.6%
1010 222
 
0.6%
Other values (579) 32524
92.8%
ValueCountFrequency (%)
982.8 1
 
< 0.1%
983 2
< 0.1%
983.3 1
 
< 0.1%
983.4 2
< 0.1%
983.5 2
< 0.1%
983.6 4
< 0.1%
983.8 2
< 0.1%
984 2
< 0.1%
984.1 1
 
< 0.1%
984.2 2
< 0.1%
ValueCountFrequency (%)
1036.5 1
 
< 0.1%
1036.4 1
 
< 0.1%
1036 1
 
< 0.1%
1035.9 1
 
< 0.1%
1035.7 2
< 0.1%
1035.6 1
 
< 0.1%
1035.5 2
< 0.1%
1035.4 2
< 0.1%
1035.3 4
< 0.1%
1035.1 2
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct617
Distinct (%)1.8%
Missing53
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.2386193
Minimum-43.4
Maximum29.1
Zeros73
Zeros (%)0.2%
Negative15770
Negative (%)45.0%
Memory size274.1 KiB
2024-03-08T12:13:17.246152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-43.4
5-th percentile-20
Q1-9.6
median2.7
Q315.3
95-th percentile22.3
Maximum29.1
Range72.5
Interquartile range (IQR)24.9

Descriptive statistics

Standard deviation14.052541
Coefficient of variation (CV)6.2773252
Kurtosis-1.1891197
Mean2.2386193
Median Absolute Deviation (MAD)12.5
Skewness-0.15275562
Sum78376.3
Variance197.47392
MonotonicityNot monotonic
2024-03-08T12:13:17.483171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.2 155
 
0.4%
17 148
 
0.4%
17.2 137
 
0.4%
17.7 129
 
0.4%
17.1 127
 
0.4%
17.9 126
 
0.4%
17.6 124
 
0.4%
17.4 124
 
0.4%
18.1 123
 
0.4%
17.8 122
 
0.3%
Other values (607) 33696
96.1%
ValueCountFrequency (%)
-43.4 1
 
< 0.1%
-34.9 1
 
< 0.1%
-34.5 3
< 0.1%
-34.4 1
 
< 0.1%
-34 1
 
< 0.1%
-33.8 3
< 0.1%
-33.6 1
 
< 0.1%
-33.5 1
 
< 0.1%
-33.4 1
 
< 0.1%
-33.1 1
 
< 0.1%
ValueCountFrequency (%)
29.1 2
 
< 0.1%
29 1
 
< 0.1%
28.8 4
< 0.1%
28.7 3
< 0.1%
28.6 2
 
< 0.1%
28.5 2
 
< 0.1%
28.4 2
 
< 0.1%
28.3 7
< 0.1%
28 1
 
< 0.1%
27.9 2
 
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct128
Distinct (%)0.4%
Missing55
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.067939673
Minimum0
Maximum45.9
Zeros33518
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:17.713724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum45.9
Range45.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84904633
Coefficient of variation (CV)12.497062
Kurtosis950.19101
Mean0.067939673
Median Absolute Deviation (MAD)0
Skewness26.543233
Sum2378.5
Variance0.72087967
MonotonicityNot monotonic
2024-03-08T12:13:17.923619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33518
95.6%
0.1 384
 
1.1%
0.2 163
 
0.5%
0.3 119
 
0.3%
0.4 91
 
0.3%
0.7 61
 
0.2%
0.5 57
 
0.2%
0.6 55
 
0.2%
0.8 54
 
0.2%
1 41
 
0.1%
Other values (118) 466
 
1.3%
(Missing) 55
 
0.2%
ValueCountFrequency (%)
0 33518
95.6%
0.1 384
 
1.1%
0.2 163
 
0.5%
0.3 119
 
0.3%
0.4 91
 
0.3%
0.5 57
 
0.2%
0.6 55
 
0.2%
0.7 61
 
0.2%
0.8 54
 
0.2%
0.9 41
 
0.1%
ValueCountFrequency (%)
45.9 2
< 0.1%
36.1 1
< 0.1%
30.9 1
< 0.1%
28.1 1
< 0.1%
28 1
< 0.1%
26.7 1
< 0.1%
25 1
< 0.1%
24.3 1
< 0.1%
23.2 1
< 0.1%
22.7 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing302
Missing (%)0.9%
Memory size274.1 KiB
NW
4943 
WNW
4876 
NE
2252 
E
2159 
W
2138 
Other values (11)
18394 

Length

Max length3
Median length2
Mean length2.2782636
Min length1

Characters and Unicode

Total characters79197
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowNNW
3rd rowNW
4th rowNNW
5th rowNNW

Common Values

ValueCountFrequency (%)
NW 4943
14.1%
WNW 4876
13.9%
NE 2252
 
6.4%
E 2159
 
6.2%
W 2138
 
6.1%
SE 2113
 
6.0%
ENE 2026
 
5.8%
ESE 2022
 
5.8%
SW 1797
 
5.1%
NNE 1687
 
4.8%
Other values (6) 8749
25.0%

Length

2024-03-08T12:13:18.164051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nw 4943
14.2%
wnw 4876
14.0%
ne 2252
 
6.5%
e 2159
 
6.2%
w 2138
 
6.2%
se 2113
 
6.1%
ene 2026
 
5.8%
ese 2022
 
5.8%
sw 1797
 
5.2%
nne 1687
 
4.9%
Other values (6) 8749
25.2%

Most occurring characters

ValueCountFrequency (%)
W 24579
31.0%
N 22164
28.0%
E 17880
22.6%
S 14574
18.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 79197
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 24579
31.0%
N 22164
28.0%
E 17880
22.6%
S 14574
18.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 79197
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 24579
31.0%
N 22164
28.0%
E 17880
22.6%
S 14574
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 24579
31.0%
N 22164
28.0%
E 17880
22.6%
S 14574
18.4%

WSPM
Real number (ℝ)

Distinct105
Distinct (%)0.3%
Missing49
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.6520206
Minimum0
Maximum12.9
Zeros150
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:13:18.387217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q10.9
median1.3
Q32
95-th percentile4.1
Maximum12.9
Range12.9
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.1991431
Coefficient of variation (CV)0.72586449
Kurtosis6.7199394
Mean1.6520206
Median Absolute Deviation (MAD)0.5
Skewness2.1770809
Sum57845.5
Variance1.4379441
MonotonicityNot monotonic
2024-03-08T12:13:18.614145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2205
 
6.3%
0.9 2204
 
6.3%
0.8 2192
 
6.3%
0.7 2024
 
5.8%
1.1 2011
 
5.7%
1.2 1881
 
5.4%
1.3 1764
 
5.0%
0.6 1573
 
4.5%
1.4 1542
 
4.4%
1.5 1396
 
4.0%
Other values (95) 16223
46.3%
ValueCountFrequency (%)
0 150
 
0.4%
0.1 118
 
0.3%
0.2 148
 
0.4%
0.3 199
 
0.6%
0.4 484
 
1.4%
0.5 1284
3.7%
0.6 1573
4.5%
0.7 2024
5.8%
0.8 2192
6.3%
0.9 2204
6.3%
ValueCountFrequency (%)
12.9 1
< 0.1%
11.8 1
< 0.1%
11.7 1
< 0.1%
11 1
< 0.1%
10.9 2
< 0.1%
10.4 1
< 0.1%
10.1 1
< 0.1%
10 1
< 0.1%
9.9 1
< 0.1%
9.7 2
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Huairou
35064 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters245448
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHuairou
2nd rowHuairou
3rd rowHuairou
4th rowHuairou
5th rowHuairou

Common Values

ValueCountFrequency (%)
Huairou 35064
100.0%

Length

2024-03-08T12:13:18.830229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:13:18.981754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
huairou 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
u 70128
28.6%
H 35064
14.3%
a 35064
14.3%
i 35064
14.3%
r 35064
14.3%
o 35064
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210384
85.7%
Uppercase Letter 35064
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 70128
33.3%
a 35064
16.7%
i 35064
16.7%
r 35064
16.7%
o 35064
16.7%
Uppercase Letter
ValueCountFrequency (%)
H 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 245448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 70128
28.6%
H 35064
14.3%
a 35064
14.3%
i 35064
14.3%
r 35064
14.3%
o 35064
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 70128
28.6%
H 35064
14.3%
a 35064
14.3%
i 35064
14.3%
r 35064
14.3%
o 35064
14.3%

Interactions

2024-03-08T12:13:06.129704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:24.370503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:27.148645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:29.818110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:32.915521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:35.506803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:38.528823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:41.661377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:44.203587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:46.998562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:49.600368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:52.694591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:56.217126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:59.424222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:02.995279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:06.393912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:24.546703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:27.344344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:30.006247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:33.087409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:35.713001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:38.785653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:41.824928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:44.398741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:47.204673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:49.771640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:53.026836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:56.418808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:59.646928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:03.202854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:06.547366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:24.729381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:27.494607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:30.209352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:33.267419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:35.949739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:38.956392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:41.942224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:44.635681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:47.405775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:50.021593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:53.222540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:56.615092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:59.843020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:03.345196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:06.746635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:24.928191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:27.682043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:30.418486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:33.423869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:36.191924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:39.136840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:42.132425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:44.803398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:47.593844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:50.188013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:53.408940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:56.830765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:00.112692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:03.565953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:06.951937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:25.069861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:27.854870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:30.920264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:33.595163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:36.385034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:39.321958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:42.294293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:45.003525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:47.740363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:50.336360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:53.646571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:57.015518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:00.320741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:03.823876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:07.233944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:25.271285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:28.041394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:31.102346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:33.745808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:36.582735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:39.504580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:42.485940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:45.211654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:47.909077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:50.572112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:53.917436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:57.269287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:00.555384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:04.043124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:07.428616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:25.459657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:28.209509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:31.257428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:33.929025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:36.757261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:39.703624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:42.634529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:45.360196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:48.068384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:50.696005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:54.126694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:57.497530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:00.769438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:04.743892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:07.628070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:25.647924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:28.341140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:31.529591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:34.097248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:36.947877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:39.907442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:42.870376image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:45.537257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:48.247306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:50.886836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:54.391682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:57.725509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:01.061312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:04.866835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:07.786213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:25.835541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:28.498177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:31.675742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:34.266629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:37.142081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:40.070164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:43.044595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:45.706345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:48.429363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:51.062544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:54.571083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:57.929837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:01.285685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:05.024847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:07.975675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:26.031192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:28.667324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:31.836287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:34.458504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:37.333210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:40.264246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:43.214298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:45.856174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:48.597415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:51.252579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:54.840294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:58.100730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:01.499160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:05.167386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:08.137292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:26.216323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:28.830892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:31.976549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:34.619039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:37.491341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:40.421762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:43.377133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:46.039979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:48.740760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:51.393631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:55.114045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:58.296658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:01.687134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:05.307391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:08.298750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:26.423445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:29.030949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:32.195602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:34.797081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:37.696495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:40.589840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:43.533612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:46.165109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:48.918801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:51.540151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:55.409818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:58.515825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:01.918192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:05.455194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:08.449867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:26.610498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:29.187663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:32.409673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:34.953623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:37.929267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:40.787976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:43.666994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:46.417911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:49.061335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:52.040779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:55.654078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:58.729884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:02.165263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:05.599357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:08.627857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:26.802905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:29.437404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:32.561743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:35.155999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:38.129121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:40.983843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:43.800129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:46.647029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:49.267945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:52.257206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:55.839459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:58.943687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:02.491174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:05.773664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:08.841805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:26.966946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:29.621503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:32.711343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:35.320589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:38.324772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:41.435805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:43.988279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:46.843078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:49.433585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:52.416995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:56.010719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:12:59.161251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:02.794139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:13:05.975202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:13:19.122265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1010.739-0.059-0.3710.7550.8510.0820.0270.544-0.185-0.3380.0030.0140.0230.0400.075
DEWP0.1011.000-0.127-0.0880.2310.1870.224-0.7690.180-0.2670.815-0.2190.023-0.0140.2550.0640.149
NO20.739-0.1271.000-0.145-0.4610.6710.6800.225-0.0830.624-0.314-0.3010.0220.100-0.0090.0460.096
No-0.059-0.088-0.1451.000-0.135-0.106-0.0740.1660.013-0.280-0.1200.1150.0180.0010.0440.0810.862
O3-0.3710.231-0.461-0.1351.000-0.107-0.182-0.420-0.030-0.0800.5630.3810.0050.242-0.1390.1040.064
PM100.7550.1870.671-0.106-0.1071.0000.904-0.118-0.0540.5100.051-0.1860.0260.114-0.0610.0410.059
PM2.50.8510.2240.680-0.074-0.1820.9041.000-0.072-0.0100.5040.000-0.2730.0140.038-0.0290.0350.053
PRES0.082-0.7690.2250.166-0.420-0.118-0.0721.000-0.0850.204-0.827-0.0180.017-0.038-0.0160.0550.146
RAIN0.0270.180-0.0830.013-0.030-0.054-0.010-0.0851.000-0.1350.047-0.030-0.005-0.0050.0490.0110.000
SO20.544-0.2670.624-0.280-0.0800.5100.5040.204-0.1351.000-0.233-0.0610.0120.124-0.1970.0410.114
TEMP-0.1850.815-0.314-0.1200.5630.0510.000-0.8270.047-0.2331.0000.1240.0170.1500.1230.1070.145
WSPM-0.338-0.219-0.3010.1150.381-0.186-0.273-0.018-0.030-0.0610.1241.000-0.0020.168-0.1480.0800.078
day0.0030.0230.0220.0180.0050.0260.0140.017-0.0050.0120.017-0.0021.0000.0000.0100.0210.000
hour0.014-0.0140.1000.0010.2420.1140.038-0.038-0.0050.1240.1500.1680.0001.0000.0000.1670.000
month0.0230.255-0.0090.044-0.139-0.061-0.029-0.0160.049-0.1970.123-0.1480.0100.0001.0000.0680.249
wd0.0400.0640.0460.0810.1040.0410.0350.0550.0110.0410.1070.0800.0210.1670.0681.0000.097
year0.0750.1490.0960.8620.0640.0590.0530.1460.0000.1140.1450.0780.0000.0000.2490.0971.000

Missing values

2024-03-08T12:13:09.135252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:13:09.614082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:13:10.137999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133107.07.03.02.0100.091.0-2.31020.3-20.70.0WNW3.1Huairou
1220133114.04.03.0NaN100.092.0-2.71020.8-20.50.0NNW1.5Huairou
2320133124.04.0NaNNaN100.091.0-3.21020.6-21.40.0NW1.8Huairou
3420133133.03.03.02.0NaNNaN-3.31021.3-23.70.0NNW2.4Huairou
4520133143.03.07.0NaN300.086.0-4.11022.1-22.70.0NNW2.2Huairou
5620133154.04.03.03.0200.085.0-4.21022.3-24.50.0N4.3Huairou
6720133163.06.033.07.0300.082.0-5.91023.1-21.90.0WNW0.6Huairou
7820133173.010.013.013.0400.071.0-2.71024.3-23.20.0NNE3.4Huairou
8920133183.013.034.038.0800.045.0-1.61025.2-23.50.0NNE4.6Huairou
910201331917.036.050.028.0700.060.0-1.11025.4-23.80.0NE4.9Huairou
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228147.07.02.06.0100.0112.015.01007.9-12.30.0WNW4.6Huairou
35055350562017228158.08.02.06.0200.0114.015.01007.3-12.30.0W4.2Huairou
35056350572017228169.018.02.07.0200.0114.015.81006.9-12.60.0WNW3.7Huairou
35057350582017228179.011.02.06.0100.0116.014.91007.0-14.30.0NW2.0Huairou
35058350592017228189.018.02.010.0200.0109.013.01007.7-14.80.0NNW2.7Huairou
350593506020172281916.028.02.019.0300.095.09.91008.6-14.10.0WNW1.8Huairou
350603506120172282021.034.04.024.0500.080.09.51008.9-14.40.0SSW1.3Huairou
350613506220172282117.033.02.039.0900.060.08.41009.3-14.60.0SE1.5Huairou
350623506320172282211.029.03.032.01400.069.08.31009.5-14.70.0ENE3.2Huairou
350633506420172282311.020.02.027.0400.077.06.71009.3-13.60.0NE1.9Huairou